getting-started

Getting started — HealthClaw + OpenClaw + your health data

The goal: a fully local, private, agent-mediated view of your own clinical records. Nothing leaves your machine unless you explicitly send it. By the end of this guide you'll have:

• An OpenClaw gateway (your local AI assistant runtime — Telegram / WhatsApp / iMessage / Slack / web)
• An open-source FHIR server holding your records (HAPI or Medplum)
• Your real EHR records pulled in via a patient-right-of-access service of your choice
• HealthClaw Guardrails sitting between any agent and that data, enforcing PHI redaction, audit trails, step-up auth, and tenant isolation
• OpenClaw agent personas (Sally-PCP, Mary-pharmacy, Dom-fitness, Kristy-scheduler) already wired to the slash-command surface and aware of every HealthClaw tool

If you already have one or more of these, the guide will tell you how to verify and skip ahead — don't reinstall what's working.

Privacy guarantee. Steps 1–4 keep your raw clinical data on your machine. The only network calls are to your chosen EHR connector (HealthEx, Flexpa, etc.) and that traffic is OAuth/SMART-on-FHIR with your explicit consent. PHI is redacted in-process before any file is written.

Prerequisites

If anything is missing, install via Homebrew (macOS) or your distro's package manager. Don't proceed until all four exist.

Step 1a — OpenClaw

OpenClaw is the local AI gateway: it runs your agent personas, exposes them on whichever channels you want (Telegram, WhatsApp, iMessage, Slack, web), and gives them access to your installed skills.

Check whether it's already installed

which openclaw                     # binary path, if present
openclaw --version                 # confirms install
ls ~/.openclaw 2>/dev/null         # workspace dir
launchctl list | grep openclaw     # macOS LaunchAgent (if running as daemon)

If openclaw --version prints a version and ~/.openclaw exists, skip to Step 1b. Otherwise continue.

Install it

The canonical install lives at https://openclaw.ai — start there. The homepage points to the current install method (npm vs Homebrew vs scripted installer); use whatever it tells you, since the install path can change between releases. The historical commands the repo has used:

# A — npm (most common)
npx -y @openclaw/cli init

# B — Homebrew tap
brew install openclaw/tap/openclaw

# C — scripted installer (check openclaw.ai for the current curl URL)

After install, log in and start the gateway:

openclaw auth login                # OAuth in browser
openclaw gateway start             # http://localhost:4319 by default
openclaw status                    # confirm "running"

For an always-on Mac mini setup (LaunchAgent so it survives reboot), see docs/mac-mini-setup.md in this repo — it has the full plist + caffeinate recipe.

Persona workspaces

OpenClaw runs each persona as a separate workspace under ~/.openclaw/workspace/. The HealthClaw repo provides a one-shot script to seed Sally / Mary / Dom / Kristy:

# (run after cloning HealthClaw — covered in Step 3)
./scripts/seed_openclaw_workspaces.sh

Don't run this yet — Step 3 will tell you when.

Step 1b — Open-source FHIR server

You need somewhere to store your records once you pull them. Two solid choices, pick one:

Option A — HAPI FHIR (recommended for first-timers)

The reference Java implementation. Easiest to run, supports R4 + R5, no auth out of the box (which is fine on localhost).

# Already running anywhere?
curl -sf http://localhost:8080/fhir/metadata >/dev/null && echo "HAPI is up"

# If not — Docker is the fastest path
docker run -d --name hapi-fhir \
  -p 8080:8080 \
  hapiproject/hapi:latest

# Wait ~30s, then verify
curl -s http://localhost:8080/fhir/metadata | head -c 200

You now have a FHIR R4 server at http://localhost:8080/fhir.

Public sandbox (zero install, but don't put real data here — it's shared and wiped weekly): https://hapi.fhir.org/baseR4.

Repo: https://github.com/hapifhir/hapi-fhir-jpaserver-starter.

Option B — Medplum (more features, OAuth-ready)

Full Medplum platform: FHIR R4 + auth + dashboard + audit. Heavier setup, but gives you a UI to browse resources.

git clone https://github.com/medplum/medplum
cd medplum
docker compose -f compose-dev.yaml up -d
# Wait 60–90s for Postgres + server to come up
open http://localhost:3000   # admin UI

Then create a project, copy the project ID, and set MEDPLUM_BASE_URL=https://api.medplum.com/fhir/R4/<project-id> (or your local URL) — HealthClaw's upstream proxy talks to it via OAuth2 client-credentials.

Repo: https://github.com/medplum/medplum · Docs: https://www.medplum.com/docs.

Verify before continuing

# Whichever you picked, this should print FHIR capability statement
curl -s http://localhost:8080/fhir/metadata | python3 -c \
  "import sys, json; d=json.load(sys.stdin); \
   print('FHIR', d.get('fhirVersion'), '·', d.get('software',{}).get('name'))"

Don't proceed without a green response — Steps 3+ assume one is reachable.

Step 2 — Connect your real records

This is the "patient right of access" step — you authenticate with your healthcare provider(s) and pull your records into the FHIR server you just set up. Records never go to the cloud. They flow EHR → connector → your machine, and stop there.

Have you already connected somewhere?

Quick checks for the common services:

# HealthEx (claude.ai integration)
# — check claude.ai → Settings → Integrations → HealthEx → "Connected"?

# Local export bundles already on disk?
ls -1t exports/healthex-*.json ~/.healthclaw/exports/healthex-*.json 2>/dev/null | head -3

# A FHIR server already populated?
curl -s "http://localhost:8080/fhir/Patient?_count=1" | grep -c '"resourceType":"Patient"'

If any of those return existing data, skip to Step 3 — you're already connected.

Pick a connector

Recommended path: HealthEx

Lowest friction, cleanest output, and HealthClaw already has a one-command import that uses the official MCP SDK and redacts PHI in-process.

1. Create a HealthEx account at https://healthex.io

2. In claude.ai → Settings → Integrations → HealthEx → Connect

3. In the HealthEx UI, connect your health systems (Epic, Cerner, CommonWell, etc.) — each one is a SMART-on-FHIR auth flow

4. Grab your HealthEx auth token (see HealthEx docs) and store it in macOS Keychain so the agents can find it later:

   security add-generic-password -s healthex -a me -w '<your-token>'
   

5. Verify connection from Claude:

   "Check when my health records were last updated"

   That triggers HealthEx:update_and_check_recent_records and confirms the link.

You'll do the actual data pull in Step 4 — Step 2 is only about getting authenticated.

Alternative: Josh Mandel's fhir-skills

A more developer-leaning path. Clone the skill repo, configure your SMART app credentials for whichever EHR you're targeting (Epic Open Sandbox is free for personal use), and Claude gets SMART-on-FHIR tools.

git clone https://github.com/jmandel/fhir-skills ~/.claude/skills/fhir-skills
# Follow the README for SMART app registration and config

Alternative: Flexpa

Hosted FHIR-as-a-service. Sign up at https://www.flexpa.com, follow their onboarding to authorize your patient identity, and you get a FHIR base URL

• access token. Plug those into HealthClaw's upstream proxy:

# In your .env
FHIR_UPSTREAM_URL=https://api.flexpa.com/fhir
# Add Flexpa's bearer token in your reverse-proxy / FHIR client

Why none of these touch the cloud (from your point of view)

The connector talks directly to your EHR — your machine in the middle, the cloud only as the OAuth provider. Once records arrive on your machine, the next step (HealthClaw) keeps them there. If you want extra paranoia, run everything inside Docker on a host with no outbound internet except the EHR endpoints.

Step 3 — Install HealthClaw Guardrails (with OpenClaw personas wired)

git clone https://github.com/aks129/HealthClawGuardrails
cd HealthClawGuardrails

# Install Python deps (uv is fastest — falls back to pip if you don't have it)
uv sync || pip install -e .

# Bootstrap your local secrets
cp .env.example .env
# Edit .env — at minimum set STEP_UP_SECRET to a random 32-byte hex string:
#   python3 -c "import secrets; print(secrets.token_hex(32))"

Wire in your FHIR server

In .env, point HealthClaw at the server you stood up in Step 1b:

FHIR_UPSTREAM_URL=http://localhost:8080/fhir         # HAPI
# or
MEDPLUM_BASE_URL=https://api.medplum.com/fhir/R4/<project-id>
MEDPLUM_CLIENT_ID=…
MEDPLUM_CLIENT_SECRET=…

Leave FHIR_UPSTREAM_URL empty if you'd rather use HealthClaw's bundled SQLite store for the first run.

Start the stack

# Full stack (Flask + Redis + MCP server + Node orchestrator)
docker-compose up -d --build

# Or, lighter: just Flask
python main.py

Verify:

curl http://localhost:5000/r6/fhir/health
# {"status":"ok","timestamp":"…","upstream":"healthy"} or "local"

Seed the OpenClaw personas

This creates four agent workspaces — Sally (PCP), Mary (pharmacy), Dom (fitness), Kristy (scheduler) — each with an AGENTS.md that already lists every HealthClaw slash command and tool:

./scripts/seed_openclaw_workspaces.sh
./scripts/update_agent_prompts.sh        # ensures each persona is current

# Install the slash-command dispatcher into ~/.healthclaw/
./scripts/bot_commands_install.sh

Each persona's AGENTS.md references ~/.healthclaw/commands.py for the mechanics and lists the slash commands the LLM should handle: /dashboard, /health, /conditions, /labs, /vitals, /meds, /allergies, /immunizations, /summary, /fhir <type>, /export, /import <path>, /help, /week, /conflicts.

Connect Claude Desktop / Claude Code

Add to your client's MCP config:

{
  "mcpServers": {
    "healthclaw-local": {
      "type": "http",
      "url": "http://localhost:3001/mcp",
      "headers": { "X-Tenant-ID": "my-health" }
    }
  }
}

(Claude Code: .mcp.json in the project root. Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json on macOS.)

Restart the client and confirm the 14 HealthClaw MCP tools appear: fhir_search, fhir_read, fhir_validate, fhir_propose_write, fhir_commit_write, curatr_evaluate, curatr_apply_fix, etc.

Cross-reference

For deeper detail on any one piece:

• fhir-r6-guardrails skill — the 14 MCP tools and their guarantees
• phi-redaction skill — what gets redacted and why
• fhir-upstream-proxy skill — talking to HAPI / Medplum / Epic
• personal-health-records skill — the conversational data-pull workflow

Step 4 — Pull data through HealthClaw

You're now authenticated to your EHR (Step 2) and HealthClaw is running (Step 3). Time to pipe records in.

Option A — One command from the agent (recommended)

In Telegram (or any OpenClaw channel), DM any persona:

/export

That invokes scripts/bot_commands.py → cmd_export(), which:

1. Reads HEALTHEX_AUTH_TOKEN from env or macOS Keychain (set in Step 2)
2. Opens an MCP Streamable HTTP session to https://api.healthex.io/mcp
3. Calls update_records + check_records_status to refresh
4. Pulls every clinical category (get_health_summary, get_conditions, get_medications, get_allergies, get_immunizations, get_vitals, get_labs, get_procedures, get_visits, search_clinical_notes)
5. Redacts PHI in-process (names → initials, addresses → state+country, identifiers → SHA-256, birthDate → YYYY, telecom → "***") — raw response never touches disk
6. Writes ~/.healthclaw/exports/healthex-<date>.json
7. Prints _meta.redaction_stats back to chat

Then ingest into your FHIR server (also via the agent):

/import ~/.healthclaw/exports/healthex-<date>.json

This POSTs to /Bundle/$ingest-context with a step-up token, validates each entry, and writes through the guardrail proxy to your HAPI / Medplum upstream.

Option B — Run the scripts directly

# Export (HealthEx → redacted bundle)
HEALTHEX_AUTH_TOKEN="$(security find-generic-password -s healthex -w)" \
  python scripts/export_healthex_mcp.py \
  --tenant-id my-health \
  --output exports/healthex-$(date +%Y-%m-%d).json

# Import (bundle → your FHIR server, gated by step-up auth)
python scripts/import_healthex.py \
  --bundle-file exports/healthex-$(date +%Y-%m-%d).json \
  --tenant-id my-health \
  --step-up-secret "$STEP_UP_SECRET"

For Fasten Health export bundles (different shape), run scripts/convert_fasten.py first to normalize.

Option C — Pull directly via Claude conversation

If HealthEx is connected to claude.ai (Step 2), just ask:

Pull my complete health history across all categories going back 15 years,
fully paginated. Then build a de-identified FHIR R4 transaction bundle with
US Core resources and write it to healthclaw-bundle-<date>.json.

Then /import the resulting file. The personal-health-records skill documents this workflow in detail.

Step 5 — Verify everything works

A 60-second checklist. Each line should produce a green response.

Liveness

# 1. OpenClaw gateway
openclaw status                           # → "running"

# 2. FHIR server
curl -sf http://localhost:8080/fhir/metadata | head -c 80

# 3. HealthClaw
curl -sf http://localhost:5000/r6/fhir/health

# 4. MCP server (only if running the Node orchestrator)
curl -sf http://localhost:3001/mcp/health || echo "(skip if not using orchestrator)"

Records actually present

# Patient count in the FHIR server
curl -s "http://localhost:8080/fhir/Patient?_summary=count" \
  | python3 -c "import sys,json; print('Patients:', json.load(sys.stdin).get('total'))"

# Record breakdown via HealthClaw (PHI-redacted)
curl -s -H "X-Tenant-ID: my-health" \
  "http://localhost:5000/r6/fhir/Bundle?type=collection&_summary=count"

Agent-side smoke test

In Claude Desktop / Code with the healthclaw-local MCP server connected:

Use the healthclaw-local tools. Get a step-up token, search for all
Patients in tenant my-health, then read one Condition and one Observation.
Confirm the responses include _mcp_summary and that PHI fields look
redacted.

You should see:

• Patient names like "E. V." instead of full names
• Identifiers prefixed with redacted:sha256:
• _mcp_summary on each result with reasoning + clinical context

Telegram/OpenClaw smoke test

DM any persona on Telegram:

/health
/summary
/conditions

Each should return a structured response paraphrased by the LLM. If /health reports any subsystem as down, fix that before moving on.

Audit trail

# Confirm every read/write you just did was logged
curl -s -H "X-Tenant-ID: my-health" \
  "http://localhost:5000/r6/fhir/AuditEvent?_count=20&_sort=-_lastUpdated" \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print('AuditEvents:', d.get('total'))"

You should see one AuditEvent per resource you've touched, including a clear actor + action + outcome.

What you now have

You — Telegram / Claude Desktop / Claude Code / Web
       │
       ▼
OpenClaw Gateway (localhost:4319)
       │   personas: Sally · Mary · Dom · Kristy
       │   skills: this repo's skills/ + your own
       ▼
HealthClaw Guardrails (localhost:5000 + MCP at :3001)
       │   PHI redaction · audit · step-up · tenant isolation
       ▼
FHIR Server (HAPI :8080 or Medplum)
       │
       ▼
Your records — never leave this machine

Troubleshooting

Next steps

Once you've got a green Step 5 checklist:

• Add wearables — see the OPEN_WEARABLES_URL env var and docs/mac-mini-setup.md for the Open Wearables sidecar (Fitbit / Oura / Whoop / Garmin / Apple Health).
• Run Curatr — the curatr_evaluate MCP tool finds data quality issues (deprecated ICD-9 codes, contradictory smoking history, antibody titers flagged as pathology, etc.). See skills/curatr/SKILL.md.
• Connect Fasten Connect for cross-network coverage — TEFCA IAS via Fasten gives one-auth records from every health system on the QHIN network. See skills/fasten-connect/SKILL.md.
• Daily review workflow — the personal-health-records skill has prompts for care-gap analysis, lab-trend review, medication reconciliation, and pre-appointment prep.

Where this skill ends and others begin

If a skill above is missing, ping @aks129 on the HealthClaw repo.